Top Hybrid Machine Learning Engineer Jobs

Design, build, and operate scalable machine learning platform capabilities across feature engineering, model training, inference, monitoring, and lifecycle management. Develop reliable software and APIs, improve production reliability and performance, resolve technical issues, and establish scalable platform architecture. Partner with engineering stakeholders to drive adoption of shared ML infrastructure and safely integrate AI/ML-enabled solutions.

Lead the technical direction and implementation of Current’s machine learning platform, covering feature pipelines, training data, model training, serving, monitoring, reproducibility, and production-readiness standards. Establish scalable workflows, improve training-serving consistency, support data scientists, define model governance contracts, evaluate build-versus-buy tooling, and drive adoption across engineering, data science, risk, marketing, and finance.

Build and maintain scalable data, feature engineering, inference, and machine learning pipelines. Deploy and manage end-to-end ML systems using MLOps tools, implement CI/CD and workflow orchestration, monitor model and data quality, troubleshoot production issues, and develop reusable platform components. Collaborate with data scientists, product, and business teams to deliver reliable AI/ML forecasting solutions across cloud and on-premises environments.

Build and deploy production machine learning systems that optimize Samsara’s global hardware supply chain. Responsibilities include demand, inventory, lead-time, spend, and supplier-risk forecasting; anomaly detection; feature engineering and ETL; real-time inference; MLOps; data infrastructure improvements; governance; and model monitoring. The role defines the AI transformation roadmap, partners with executive and cross-functional stakeholders, mentors scientists, and drives adoption of AI-enabled operational workflows.

Own the reliability and quality of an AI copilot for a trading platform. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement workflows. Partner with engineering and product to create safe, auditable tool interactions and improve assistant behavior. Develop domain expertise in trading, risk, margin, execution, and portfolio reasoning while operating across model, backend, and frontend systems.

Develop and productize machine learning and digital signal processing algorithms for speech and audio applications, including noise suppression, echo cancellation, beamforming, spatial audio, and enhancement. Research modern deep learning approaches, prototype with Python or MATLAB, and deploy optimized real-time solutions on consumer hardware. Collaborate across research, systems engineering, and product development to deliver robust audio technologies at scale.

Leads end-to-end machine learning initiatives for Block’s support experiences, including conversational AI, chatbots, recommendation systems, and agent tools. Defines ML roadmaps, guides research into LLMs, RAG, fine-tuning, and real-time inference, and designs, deploys, and maintains production models. The role requires strategic technical leadership, cross-functional influence, stakeholder communication, and extensive experience delivering applied ML and generative AI systems.

Design, train, validate, and deploy machine learning and deep learning models for e-procurement use cases including prediction, ranking, recommendations, entity resolution, document intelligence, and automation. Perform exploratory data analysis, feature engineering, experimentation, model evaluation, and error analysis. Build maintainable Python and SQL workflows, collaborate with engineering and product teams, and communicate technical findings to diverse stakeholders.

Develop and deploy AI-driven annotation, labeling, and captioning systems for robotics, computer vision, autonomy, and Vision-Language-Action datasets. Train and evaluate machine learning models, build scalable data pipelines, integrate human-in-the-loop workflows, and improve annotation quality. The role requires production software engineering, distributed systems, sensor-data expertise, and experience with computer vision or autonomous systems. Work is onsite in Irving, Texas, with relocation assistance and visa sponsorship available.

Own the ML platform powering production training pipelines, model serving, deployment, orchestration, observability, and multi-region scaling. Drive major infrastructure initiatives, improve reliability and cost efficiency, lead incident response, collaborate across teams, and set the technical vision for ML operations. The role is hands-on, requiring production distributed-systems expertise, Kubernetes and cloud infrastructure knowledge, technical leadership, mentoring, and strong communication.